Bibliographic record
Abstract
In 1942, the Canadian government forced more than 21,000 Japanese Canadians from their homes in British Columbia. They were told to bring only one suitcase each and officials vowed to protect the rest. Instead, Japanese Canadians were dispossessed, all their belongings either stolen or sold. The definitive statement of a major national research partnership, Landscapes of Injustice reinterprets the internment of Japanese Canadians by focusing on the deliberate and permanent destruction of home through the act of dispossession. All forms of property were taken. Families lost heirlooms and everyday possessions. They lost decades of investment and labour. They lost opportunities, neighbourhoods, and communities; they lost retirements, livelihoods, and educations. When Japanese Canadians were finally released from internment in 1949, they had no homes to return to. Asking why and how these events came to pass and charting Japanese Canadians' diverse responses, this book details the implications and legacies of injustice perpetrated under the cover of national security. In Landscapes of Injustice the diverse descendants of dispossession work together to understand what happened. They find that dispossession is not a chapter that closes or a period that neatly ends. It leaves enduring legacies of benefit and harm, shame and silence, and resilience and activism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".